At e-commerce websites, primary means of navigation involve user initiated activities like browsing a category, searching by entering keywords, or filtering search results by selecting values for a set of attributes a system provides. These actions often require users to understand an ontology used in organizing an inventory to guess keywords that may lead to desired results. This process may be frustrating for users who are not familiar with content of the site or for users who only have a general sense of what they are looking for.
Recommendations provide an alternative approach to assist users in accessing relevant content. Unlike search engines that aim to answer user-formulated queries, recommendation engines provide content without requiring direct user input. Instead, conventional recommendation engines use inferred user interest to identify and provide content. These conventional recommendation engines infer user interest based on indirect information sources such as short-term session history, long-term user behavioral data, ontology the site uses to organize inventory, and the state of active inventory.